diff options
Diffstat (limited to 'numpy/lib/histograms.py')
| -rw-r--r-- | numpy/lib/histograms.py | 106 |
1 files changed, 24 insertions, 82 deletions
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py index 1a9b41ced..35745e6dd 100644 --- a/numpy/lib/histograms.py +++ b/numpy/lib/histograms.py @@ -506,8 +506,8 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): with non-normal datasets. 'scott' - Less robust estimator that that takes into account data - variability and data size. + Less robust estimator that takes into account data variability + and data size. 'stone' Estimator based on leave-one-out cross-validation estimate of @@ -562,7 +562,8 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): below, :math:`h` is the binwidth and :math:`n_h` is the number of bins. All estimators that compute bin counts are recast to bin width using the `ptp` of the data. The final bin count is obtained from - ``np.round(np.ceil(range / h))``. + ``np.round(np.ceil(range / h))``. The final bin width is often less + than what is returned by the estimators below. 'auto' (maximum of the 'sturges' and 'fd' estimators) A compromise to get a good value. For small datasets the Sturges @@ -580,7 +581,7 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): datasets. The IQR is very robust to outliers. 'scott' - .. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}} + .. math:: h = \sigma \sqrt[3]{\frac{24 \sqrt{\pi}}{n}} The binwidth is proportional to the standard deviation of the data and inversely proportional to cube root of ``x.size``. Can @@ -597,7 +598,7 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): does not take into account data variability. 'sturges' - .. math:: n_h = \log _{2}n+1 + .. math:: n_h = \log _{2}(n) + 1 The number of bins is the base 2 log of ``a.size``. This estimator assumes normality of data and is too conservative for @@ -606,9 +607,9 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): 'doane' .. math:: n_h = 1 + \log_{2}(n) + - \log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}}) + \log_{2}\left(1 + \frac{|g_1|}{\sigma_{g_1}}\right) - g_1 = mean[(\frac{x - \mu}{\sigma})^3] + g_1 = mean\left[\left(\frac{x - \mu}{\sigma}\right)^3\right] \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}} @@ -670,15 +671,14 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None): def _histogram_dispatcher( - a, bins=None, range=None, normed=None, weights=None, density=None): + a, bins=None, range=None, density=None, weights=None): return (a, bins, weights) @array_function_dispatch(_histogram_dispatcher) -def histogram(a, bins=10, range=None, normed=None, weights=None, - density=None): +def histogram(a, bins=10, range=None, density=None, weights=None): r""" - Compute the histogram of a set of data. + Compute the histogram of a dataset. Parameters ---------- @@ -703,16 +703,6 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, computation as well. While bin width is computed to be optimal based on the actual data within `range`, the bin count will fill the entire range including portions containing no data. - normed : bool, optional - - .. deprecated:: 1.6.0 - - This is equivalent to the `density` argument, but produces incorrect - results for unequal bin widths. It should not be used. - - .. versionchanged:: 1.15.0 - DeprecationWarnings are actually emitted. - weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count @@ -727,8 +717,6 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability *mass* function. - Overrides the ``normed`` keyword if given. - Returns ------- hist : array @@ -890,46 +878,15 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, n = np.diff(cum_n) - # density overrides the normed keyword - if density is not None: - if normed is not None: - # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6) - warnings.warn( - "The normed argument is ignored when density is provided. " - "In future passing both will result in an error.", - DeprecationWarning, stacklevel=3) - normed = None - if density: db = np.array(np.diff(bin_edges), float) return n/db/n.sum(), bin_edges - elif normed: - # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6) - warnings.warn( - "Passing `normed=True` on non-uniform bins has always been " - "broken, and computes neither the probability density " - "function nor the probability mass function. " - "The result is only correct if the bins are uniform, when " - "density=True will produce the same result anyway. " - "The argument will be removed in a future version of " - "numpy.", - np.VisibleDeprecationWarning, stacklevel=3) - - # this normalization is incorrect, but - db = np.array(np.diff(bin_edges), float) - return n/(n*db).sum(), bin_edges - else: - if normed is not None: - # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6) - warnings.warn( - "Passing normed=False is deprecated, and has no effect. " - "Consider passing the density argument instead.", - DeprecationWarning, stacklevel=3) - return n, bin_edges + + return n, bin_edges -def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None, - weights=None, density=None): +def _histogramdd_dispatcher(sample, bins=None, range=None, density=None, + weights=None): if hasattr(sample, 'shape'): # same condition as used in histogramdd yield sample else: @@ -940,14 +897,13 @@ def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None, @array_function_dispatch(_histogramdd_dispatcher) -def histogramdd(sample, bins=10, range=None, normed=None, weights=None, - density=None): +def histogramdd(sample, bins=10, range=None, density=None, weights=None): """ Compute the multidimensional histogram of some data. Parameters ---------- - sample : (N, D) array, or (D, N) array_like + sample : (N, D) array, or (N, D) array_like The data to be histogrammed. Note the unusual interpretation of sample when an array_like: @@ -978,20 +934,16 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None, If False, the default, returns the number of samples in each bin. If True, returns the probability *density* function at the bin, ``bin_count / sample_count / bin_volume``. - normed : bool, optional - An alias for the density argument that behaves identically. To avoid - confusion with the broken normed argument to `histogram`, `density` - should be preferred. weights : (N,) array_like, optional An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. - Weights are normalized to 1 if normed is True. If normed is False, + Weights are normalized to 1 if density is True. If density is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. Returns ------- H : ndarray - The multidimensional histogram of sample x. See normed and weights + The multidimensional histogram of sample x. See density and weights for the different possible semantics. edges : list A list of D arrays describing the bin edges for each dimension. @@ -1018,7 +970,7 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None, sample = np.atleast_2d(sample).T N, D = sample.shape - nbin = np.empty(D, int) + nbin = np.empty(D, np.intp) edges = D*[None] dedges = D*[None] if weights is not None: @@ -1029,7 +981,7 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None, if M != D: raise ValueError( 'The dimension of bins must be equal to the dimension of the ' - ' sample x.') + 'sample x.') except TypeError: # bins is an integer bins = D*[bins] @@ -1049,13 +1001,13 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None, smin, smax = _get_outer_edges(sample[:,i], range[i]) try: n = operator.index(bins[i]) - + except TypeError as e: raise TypeError( "`bins[{}]` must be an integer, when a scalar".format(i) ) from e - - edges[i] = np.linspace(smin, smax, n + 1) + + edges[i] = np.linspace(smin, smax, n + 1) elif np.ndim(bins[i]) == 1: edges[i] = np.asarray(bins[i]) if np.any(edges[i][:-1] > edges[i][1:]): @@ -1103,16 +1055,6 @@ def histogramdd(sample, bins=10, range=None, normed=None, weights=None, core = D*(slice(1, -1),) hist = hist[core] - # handle the aliasing normed argument - if normed is None: - if density is None: - density = False - elif density is None: - # an explicit normed argument was passed, alias it to the new name - density = normed - else: - raise TypeError("Cannot specify both 'normed' and 'density'") - if density: # calculate the probability density function s = hist.sum() |